Summary

We argue that an activity-based viewpoint allows to create a shared understanding as well as a common language for RE artifact quality. Furthermore, we argue that ABRE-QMs enable to precisely reason about which quality factors exist and to precisely evaluate the consequences of a quality factor. We also argue that ABRE-QMs enable to more adequately define RE artifact quality and provide a first notion of a complete quality model.

However, open questions exist towards the existence of one common quality definition, the right granularity of model elements, human compensation, transitive, feedback and non-linear impacts, and, more fundamentally, the extent to which we (are able to) know about the impact of quality factors in RE artifacts.

Regarding automatic requirements smell detection, automatic approaches have their advantages, namely effort, speed, and consistency. However, they assume the existence of an explicit quality definition. They furthermore struggle compensating for noise and the limitations of the state-of-the-art NLP methods. In addition, automatic approaches lack the knowledge about the project scope (what do the stakeholders want?), as well as the domain knowledge that is not captured in RE artifacts.

Automatic requirements smell detection can detect violations of various quality criteria. However, for all of the criteria, there are also aspects that are not covered.

This, again, suggests the need for combining automatic with manual approaches, as we propose in RQ 3. In addition, the question whether the number of findings is a accurate predictor of project success is still an open question.

CHAPTER 6

Conclusions and Outlook

Looking back at the problem statement, we summarize our results and provide an outlook to future work.

6.1. Conclusions

RE artifacts play a central role in software development. Their quality can be crucial for the project success. But, as discussed in Chapter 1, we currently have a limited understanding of what high quality RE artifacts are and need more efficient methods to control RE artifact quality in practice. This thesis approaches these two problems in two directions: First, we contribute a novel model to define RE artifact quality, and second, we present a more efficient method to execute QC for RE artifacts. In the following, we come back to the research questions and summarize our results.

6.1.1. Definition of RE Artifact Quality

As we argue in Chapter 1, existing definitions for RE artifact quality lack precise reasoning and adequacy for various contexts. In addition, whether existing sets of quality characteristics are complete is an open question.

We refined this problem into two research questions. To precisely define RE artifact quality in RQ 1, we contribute a meta model for RE artifact quality from a quality-in-use perspective. To create valid quality models for RQ 2, we contribute a validation framework and three applications thereof.

RQ 1: How can we precisely define quality for RE artifacts? This thesis contributes Activity-based RE Quality Models (ABRE-QM), an approach to define RE artifact quality, based on the understanding that high quality RE artifacts are those that are efficient and effective to use.

Based on this notion of quality-in-use, ABRE-QMs enable to precisely reason about quality factors and their impact on a stakeholder’s activities. In addition, the quality-in-use perspective enables to model quality for a given context. Lastly, this

6.1. Conclusions

thesis provides a first understanding of completeness for an RE artifact quality model.

In summary, this thesis answers RQ 1 through a meta model that defines RE artifact quality from a quality-in-use perspective.

RQ 2: How can we create valid quality models? We argue that the main advantage of the ABRE-QM is its precise reasoning. In RQ 2, we contribute a framework for creating valid ABRE-QMs and show its use in an interview, a case study, and an experiment.

We also contribute a brief discussion of advantages and disadvantages according to our experience during these studies. We argue that interviews are best suited for validating a large set of known quality factors in a given context. For example, we contribute an interview study to validate an existing use case guideline. In contrast, case studies enable to study more unknown fields. In our case, we contributed a case study to better understand quality factors for maintenance of RE artifacts.

Lastly, experiments can study individual quality factors in depth. However, due to their artificial setting and expensive setup, we argue to use this method for subtle, unclear quality factors. For example, we contributed an experiment that analyzed the effects of passive voice in RE artifacts.

In summary, this thesis answers RQ 2 through providing a framework for interviews, case studies and experiments. In addition, we discuss advantages, disadvantages and applications based on a set of contributing studies.

6.1.2. Efficient Methods for RE Artifact Quality Control

As a second problem, we argued that quality control of RE artifact quality struggles in practices, even when agreed upon quality factors. To more efficiently detect quality factors for RQ 3, we contribute requirements smells and automatic requirements smell detection. To understand benefits and limitations of requirements smell detection in RQ 4, we study the accuracy, the applicability, the benefits and the limitations of such an approach.

RQ 3: How can we efficiently ensure quality factors? We contribute a case study on RE artifact quality, indicating that besides a precise quality definition, the core problems lie in efficiency, speed and sustainability of the QC process. To address the issues, we furthermore contribute a definition of requirements smells as automatically detectable quality factors. In order to make QC more efficient we propose automatic requirements smell detection, for which we also provide a prototype as a technical validation. In addition, we contribute a smell taxonomy and a method that combines manual and automatic QA.

In summary, this thesis answers RQ 3 through combining efficient automatic require-ments smell detection with manual detection of quality factors.

RQ 4: What are the benefits and limitations of requirements smell detection? To understand the precision, applicability, benefits and limitations, we contribute the results of a multi-case study with 12 industrial projects from three companies and an academic case, where we analyzed requirements of 51 different teams. In total, we analyzed requirements of the size of more than 265,000 words. To determine, how reliably the detected smells indicate quality problems, we measure the precision of our analysis. The requirements smell detection precision varies strongly between 0.26 and0.96, averaging around0.6. Furthermore, to understand the number of

82

6. Conclusions and Outlook